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Incorporating Neighborhood Information and Sentence Embedding Similarity into a Repost Prediction Model in Social Media Networks 11th

Incorporating Neighborhood Information and Sentence Embeddi...

引用

11th international conference on Computational data and Social Networks, CSoNet 2022

作者： Qiang, Zhecheng Pasiliao, Eduardo L. Semenov, Alexander Zheng, Qipeng P. Department of Industrial Engineering and Management Systems University of Central Florida OrlandoFL United States Munitions Directorate Air Force Research Laboratory ShalimarFL United States Department of Industrial and Systems Engineering University of Florida GainesvilleFL United States

ISBN: (纸本)9783031263026

Predicting repost behaviors within social media networks plays an important role in human activities analysis and influence maximization decision making. Traditional methods for repost prediction can be categorized into stochastic diffusion based models and user profile or content features based machine learning models. In this paper, we propose a new framework combining user profile, content similarity and the neighborhood information around each target link as input features to make the prediction. Here neighborhood information can be interpreted as the combination of neighbors’ user profile. Two different kinds of graph based combination models are introduced in the article. After collecting the input features, we implement the state-of-the-art machine learning methods, e.g., Logistic Regression, K-nearest Neighbors, Gaussian Naive Bayes, Deep Neural Network, Random Forest, XGBoosting and Stacking Model to predict repost probability. We evaluate our model on real dataset Weibo to compare the performance with different features and machine learning methods. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Efficient active learning with generalized linear models

Efficient active learning with generalized linear models

引用

11th international conference on Artificial Intelligence and Statistics, AISTATS 2007

作者： Lewi, Jeremy Butera, Robert Paninski, Liam Bioengineering Georgia Institute of Technology United States Electrical and Computer Engineering Georgia Institute of Technology United States Department of Statistics Columbia University United States

Active learning can significantly reduce the amount of training data required to fit parametric statistical models for supervised learning tasks. Here we present an efficient algorithm for choosing the optimal (most informative) query when the output labels are related to the inputs by a generalized linear model (GLM). the algorithm is based on a Laplace approximation of the posterior distribution of the GLM's parameters. the algorithm requires only low-rank matrix manipulations and a single two-dimensional search to choose the optimal query and has complexity O(n 2) (with n the dimension of the feature space), making active learning with GLMs feasible even for high-dimensional feature spaces. In certain cases the two-dimensional search may be reduced to a one-dimensional search, further improving the algorithm's efficiency. Simulation results show that the model parameters can be estimated much more efficiently using the active learning technique than by using randomly chosen queries. We compute the asymptotic posterior covariance semi-analytically and demonstrate that the algorithm empirically achieves this asymptotic convergence rate, which is generally better than the convergence rate in the random-query setting. Finally, we generalize the approach to efficiently handle both output history effects (for applications to time-series models of autoregressive type) and slow, non-systematic drifts in the model parameters.

关键词： Parameter estimation

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Good News vs. Bad News: What are they talking about? 11

Good News vs. Bad News: What are they talking about?

引用

11th international conference on Recent Advances in Natural Language Processing, RANLP 2017

作者： Kanishcheva, Olga Bobichev, Victoria Intelligent Computer Systems Department National Technical University KhPI Ukraine Department of Informatics and Systems Engineering Technical University of Moldova Moldova

ISBN: (纸本)9789544520489

Today's massive news streams demand the automate analysis which is provided by various online news explorers. However, most of them do not provide sentiment analysis. the main problem of sentiment analysis of news is the differences between the writers and readers attitudes to the news text. News can be good or bad but have to be delivered in neutral words as pure facts. Although there are applications for sentiment analysis of news, the task of news analysis is still a very actual problem because the latest news impacts people's lives daily. In this paper, we explored the problem of sentiment analysis for Ukrainian and Russian news, developed a corpus of Ukrainian and Russian news and annotated each text using one of three categories: positive, negative and neutral. Each text was marked by at least three independent anno-tators via the web interface, the inter-annotator agreement was analyzed and the final label for each text was computed. these texts were used in the machine learning experiments. Further, we investigated what kinds of named entities such as Locations, Organizations, Persons are perceived as good or bad by the readers and which of them were the cause for text annotation ambiguity. © 2018 Association for Computational Linguistics (ACL). All rights reserved.

关键词： Sentiment analysis

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A Median Filtering Forensics CNN Approach Based on Local Binary Pattern 11th

A Median Filtering Forensics CNN Approach Based on Local Bin...

引用

11th international conference on Computer engineering and Networks, CENet2021

作者： Zhu, Tao Gu, Haiyan Chen, Zenan Department of Computer Information and Cyber Security Jiangsu Police Institute Nanjing210031 China Jiangsu Province Electronic Data Forensics and Analysis Engineering Research Center Nanjing210031 China Key Laboratory of Digital Forensics of Jiangsu Provincial Public Security Department Nanjing210031 China

ISBN: (纸本)9789811665530

Median filtering forensics has gradually become a research hotspot because of the wide application of the traditional median filtering (MF) in image tampering and anti-forensics. the difficulty of traditional median filtering based on machine learning forensics is feature extraction which is a manual selection process, and the Convolutional Neural Network (CNN) cannot also well perceive the traces left by median filtering straightly. By taking the Local Binary Pattern (LBP) data of an image as a presentation of the streaking artifact that is a very strong indication for median filtering, a median filtering forensics CNN approach with LBP is proposed, which can automatically learn and get median filtering features from image. Different from traditional CNN, an LBP perception layer is added before the following deep learning layers. then, five convolution layers constitute the feature extraction group. Finally, the classifier group is composed of three full connected layers to decide whether the image is median filtered or not. the proposed approach is tested on several experiments and the experimental results demonstrate its effectiveness. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Local binary pattern

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Why Use Evolving Neuro-Fuzzy and Spiking Neural Networks for incremental and explainable learning of time series? A case study on predictive modelling of trade imports and outlier detection

Why Use Evolving Neuro-Fuzzy and Spiking Neural Networks for...

引用

international IEEE conference on intelligent Systems, IS

作者： Iman Abouhassan Nikola Kasabov George Popov Roumen Trifonov Dept. of Information Technology in Industry Technical University of Sofia Sofia Bulgaria KEDRI- SECMS Auckland University of Technology Auckland New Zealand Ulster Universty UK

ISBN: (纸本)9781665492768

What algorithms to choose for an incremental, predictive, and more importantly, explainable learning and modelling of time series data, and specifically – economic and financial time series? Will these algorithms reveal and explain abnormality in time series over time? this problem is part of the challenges in the area of explainable AI and life-long learning systems. three widely used evolving connectionist systems (ECOS) that offer a solution to the above problems, are compared in the paper. the first two models, EFuNN and DENFIS, are neuro-fuzzy models that deal with vector-based data, while the spiking neural network model NeuCube deals with temporal and spatio-temporal data. the case study data used to demonstrate the qualities of these techniques is time series data related to Bulgarian petroleum oil imports from various trading partners. Graphical visualization enhances the deep analysis, pattern recognition, and knowledge extraction from the models. Conclusions are drawn in the sense that each of these techniques reveals different aspects of the data and the problem in hand and a single model solution of a complex problem is never going to be complete. Future work outlines integration of time series and on-line news to achieve a better predictive accuracy and a better understanding of the data.

关键词： learning systems Biological system modeling Oils Time series analysis Neural networks Predictive models Prediction algorithms

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the influence of sampling on imbalanced data classification 8

The influence of sampling on imbalanced data classification

引用

8th Brazilian conference on intelligent Systems, BRACIS 2019

作者： Barella, Victor H. Garcia, Luís P.F. De Carvalho, André C.P.L.F. ICMC/University of São Paulo São Carlos Brazil CIC/University of Brasilia Brasília Brazil

ISBN: (纸本)9781728142531

Classification tasks using imbalanced data are not challenging on their own. When the classes are linearly separable, a regular classification algorithm usually induces predictive models able to distinguish the classes properly. Imbalanced data poses difficulty for the minority class when the training sets have classes overlapping or a complex border decision. Assessing these characteristics is fundamental to understand the classification task difficulty and to choose adequate pre-processing techniques for imbalanced data. Measures able to identify the complexity of a classification task for a given dataset have been proposed. these measures use different criteria to identify how difficult it is to induce a classifier from a dataset. In this paper, we investigate the use of data complexity measures to estimate the best sample size for data imbalance pre-processing techniques. For such, this paper assesses the predictive performance and the data complexity of real datasets after applying pre-processing techniques using different sample sizes. According to experiments, the data complexity measures are a tool to help in choosing a proper sample size to improve the predictive performance of the classifiers. We also observe that only the difficulty of predicting the minority class is not enough when dealing with sampling. As an alternative to deal with this deficiency, we suggest a combination of the data complexity of both classes. © 2019 IEEE.

关键词： Sampling

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Dynamic hand gesture recognition based on randomized self-organizing map algorithm 11th

引用

11th international conference on Algorithmic learning theory, ALT 2000

作者： Tobely, Tarek El. Yoshiki, Yuichiro Tsuda, Ryuichi Tsuruta, Naoyuki Amamiy, Makoto Department of Intelligent Systems Graduate School of Information Science and Electrical Engineering Kyushu University 6-1 Kasuga-Koen Kasuga Fukuoka816 Japan Department of Electronic Engineering Graduate School of Electronics Engineering Fukuoka University 8-19-1 Nanakuma Jonan Fukuoka814-0180 Japan

ISBN: (纸本)9783540412373

Gesture recognition is an appealing tool for natural interface with computers especially for physically impaired persons. In this paper, it is proposed to use Self-Organized Map (SOM) to recognize the posture images of hand gestures. Since the competition algorithm of SOM allows alleviating many difficulties associated with gesture recognition. However, it is required to reduce the recognition time of one image in SOM network to the range of normal video camera rates, this permits the network to accept dynamic input images and to perform on-line recognition for hand gestures. To achieve this, the Randomized Self-Organizing Map algorithm (RSOM) is proposed as a new recognition algorithm for SOM. With RSOM algorithm, the recognition time of one image reduced to 12.4 % of the normal SOM competition algorithm with 100 % accuracy and allowed the network to recognize images within the range of normal video rates. the experimental results to recognize six dynamic hand gestures using RSOM algorithm is presented. © Springer-Verlag Berlin Heidelberg 2000.

关键词： Gesture recognition

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An Agent-Based Support System for Suburban Railway Traffic Control

An Agent-Based Support System for Suburban Railway Traffic C...

引用

international conference on intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Taomei Zhu Jose Manuel Mera Xiuqing Guo Yidan Xing Wei Nai Electronics and Information Engineering Tongji Zhejiang College Jiaxing Zhejiang China Railway Technology Research Center (CITEF) ETSII of Technical University of Madrid (UPM) Madrid Spain Tongji Zhejiang College Jiaxing China

Suburban railway lines are emerging in China in the last few years. Not as definite as in high-speed railway or urban rail transit, the applications of signaling and traffic control system in suburban railway have not been strictly defined. Based on the investigation of the traffic scale and its characteristics in suburban railway, and traffic control systems implemented respectively in national railway and urban rail transit, an agent-based system, named D-Agent, is presented to support suburban railway traffic control and management. It consists of six basic modules: local data, knowledge base, skills, reasoning mechanism, data processing, and communication. Infrastructure characteristics, train performances, safety and operational restrictions are stored in local data and knowledge base. Functions of traffic control and adaptive support become the skills of D-Agent, and all possible interfaces of traffic control system are learned as the languages of D-Agent for communication with other systems. through continuous learning, D-Agent can be trained to support the traffic control system or dispatcher in handling traffic problems, as well in adapting to different signaling systems that may be applied in suburban railway.

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A Batch-Normalized Deep Neural Networks and its Application in Bearing Fault Diagnosis

A Batch-Normalized Deep Neural Networks and its Application ...

引用

international conference on intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Jiaqi Zheng Hongjun Sun Xiaojing Wang Jun Liu Caizhi Zhu School of mechanical engineering and automation Shanghai University 99 Shangda Road Shanghai P.R.China Shanghai University Shanghai China

At present, in the field of fault diagnosis, deep learning has shown state-of-the-art performance in processing mechanical big data. this paper studies the deep neural networks(DNN) model based on auto-encoder, which has high performance in bearing fault diagnosis. However, the traditional structure of stacked auto encoders has the problem of internal covariant transfer, that inhibits the training efficiency and generalization ability of the network. To overcome the aforementioned deficiency and further explore the performance of DNN, a batch normalization layer is employed in the fully connected layer of the DNN during training, so the network can obtain the stable distribution of activation values. therefore, this paper proposes a new intelligent diagnosis method named batch normalization deep neural networks(BN-DNN). Finally, the experimental results show that: (1) the performance of BN-DNN is better than DNN. (2) BN-DNN can directly process the raw vibration signals, and the diagnostic accuracy can be maintained above 99% under different working conditions.

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the Classification of Brain Tumours by Means of Feature-Based Transfer learning 1

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11th international conference on Robot Intelligence Technology and Applications, RiTA 2023

作者： Wu, Chengzhangzheng Yang, Junqing Liu, Taimingwang Tan, Andrew Luo, Yang Razman, Mohd Azraai Mohd Majeed, Anwar P. P. Abdul School of Robotics Xi’an Jiaotong-Liverpool University Taicang Suzhou215400 China School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Taicang Suzhou215400 China Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Pahang26600 Malaysia School of Engineering and Technology Sunway University Selangor Bandar Sunway47500 Malaysia

ISBN: (数字)9783031706875

ISBN: (纸本)9783031706868

Brain tumours are abnormal growths of cells in the brain and can be life-threatening if not detected early. Traditionally, radiologists manually assess magnetic resonance imaging (MRI) scans of the brain to identify and evaluate brain tumours;however, this process is prone to misinterpretation. this study investigates the application of a deep learning technique known as feature-based transfer learning to automate brain tumour detection from MRI images. A dataset of MRI scans labelled with different types of brain tumours was utilised in the study, in which a MobileNet pre-trained convolutional neural network was used to extract discriminative features from the images. the different classes of the tumors were then classified by three vanilla machine learning models, i.e., kNearest Neighbors (kNN), Support Vector Machine (SVM) and Logistic Regression (LR). the study showed that the MobileNet + LR pipeline could distinguish the classes well. the proposed method demonstrates its potential for augmenting and enhancing radiologist assessment of medical imaging. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

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